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          Kano 模型
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        <p><strong>1. 卡诺模型简介 </strong></p>
<blockquote>
<p><strong> 卡诺模型（KANO 模型）</strong> 是对用户需求分类和优先排序的有用工具，以分析用户需求对用户满意的影响为基础，体现了产品性能和用户满意之间的非线性关系。在卡诺模型中，将产品和服务的质量特性分为四种类型：⑴必备属性；⑵期望属性；⑶魅力属性；⑷无差异属性。</p>
</blockquote>
<span id="more"></span>

<ul>
<li><strong> 魅力属性：</strong> 用户意想不到的，如果不提供此需求，用户满意度不会降低，但当提供此需求，用户满意度会有很大提升；</li>
<li><strong> 期望属性 </strong>：当提供此需求，用户满意度会提升，当不提供此需求，用户满意度会降低；</li>
<li><strong> 必备属性：</strong> 当优化此需求，用户满意度不会提升，当不提供此需求，用户满意度会大幅降低；</li>
<li> 无差异因素：无论提供或不提供此需求，用户满意度都不会有改变，用户根本不在意；</li>
<li><strong> 反向属性：</strong> 用户根本都没有此需求，提供后用户满意度反而会下降 </li>
</ul>
<p>KANO 问卷对每个质量特性都由正向和负向两个问题构成，分别测量用户在面对存在或不存在某项质量特性时的反应。</p>
<img data-src="/archives/394b329c/cb9bf224a6dc8ab7e3d9d18e6ab6a929_hd.jpg" class>

<img data-src="/archives/394b329c/c9b6432ce36c51f7d55fa1242b13ea1b_hd.jpg" class>

<p> 除了对于 Kano 属性归属的探讨，还可以通过对于功能属性归类的百分比，计算出 Better-Worse 系数，表示某功能可以增加满意或者消除很不喜欢的影响程度。</p>
<ul>
<li> 增加后的满意系数 Better/SI=（A+O）/(A+O+M+I)</li>
<li> 消除后的不满意系数 Worse/DSI=-1*（O+M）/(A+O+M+I)</li>
</ul>
<p><strong>Better，可以被解读为增加后的满意系数。</strong>better 的数值通常为正，代表如果提供某种功能属性的话，用户满意度会提升；正值越大 / 越接近 1，表示对用户满意上的影响越大, 用户满意度提升的影响效果越强，上升的也就更快。</p>
<p><strong>Worse，则可以被叫做消除后的不满意系数。</strong> 其数值通常为负，代表如果不提供某种功能属性的话，用户的满意度会降低；值越负向 / 越接近 -1，表示对用户不满意上的影响最大，满意度降低的影响效果越强，下降的越快。</p>
<p> 因此，根据 better-worse 系数，对系数绝对分值较高的功能 / 服务需求应当优先实施。</p>
<img data-src="/archives/394b329c/f5b0a46d7660f9d21d403fb0f10055c1_hd.jpg" class>

<p> 根据 better-worse 系数值，将散点图划分为四个象限。</p>
<ul>
<li> 第一象限表示：better 系数值高，worse 系数绝对值也很高的情况。落入这一象限的属性，称之为是期望属性，即表示产品提供此功能，用户满意度会提升，当不提供此功能，用户满意度就会降低，这是质量的竞争性属性，应尽力去满足用户的期望型需求。提供用户喜爱的额外服务或产品功能，使其产品和服务优于竞争对手并有所不同，引导用户加强对本产品的良好印象；</li>
<li> 第二象限表示：better 系数值高，worse 系数绝对值低的情况。落入这一象限的属性，称之为是魅力属性，即表示不提供此功能，用户满意度不会降低，但当提供此功能，用户满意度和忠诚度会有很大提升；</li>
<li> 第三象限表示：better 系数值低，worse 系数绝对值也低的情况。落入这一象限的属性，称之为是无差异属性，即无论提供或不提供这些功能，用户满意度都不会有改变，这些功能点是用户并不在意的功能。</li>
<li> 第四象限表示：better 系数值低，worse 系数绝对值高的情况。落入这一象限的属性，称之为是必备属性，即表示当产品提供此功能，用户满意度不会提升，当不提供此功能，用户满意度会大幅降低；说明落入此象限的功能是最基本的功能，这些需求是用户认为我们有义务做到的事情。</li>
</ul>
<p> 同类型功能之间，建议优先考虑 better 系数较高，worse 系数较低的。</p>
<p> 在产品开发时，功能优先级的排序一般是：必备属性 &gt; 期望属性 &gt; 魅力属性 &gt; 无差异属性。</p>
<p> 但实际需要考虑产品的市场策略，如期望属性和魅力属性是可以击中用户的爽点或痒点的，在争取市场份额上期望属性和魅力属性更为重要，且可以考虑作为产品卖点进行包装营销。</p>
<p><strong>2. 卡诺模型实操 </strong></p>
<p><strong>2.1 问卷编写：</strong></p>
<p> 由于 KANO 模型问卷均需要了解以下两个方面：用户对于产品 / 服务具备某功能时的评价（态度）和产品 / 服务不具备某功能时的评价（态度），需要分别正向和反向地询问用户。需要注意：</p>
<p>① KANO 问卷中与每个功能点相关的题目都有正反两个问题，正反问题之间的区别需注意强调，防止用户看错题意；</p>
<p>② 在实际题目设置上，当功能点个数比较多（大于 5 个时）或功能点的差异不大时，有相似之处时，建议对用户进行分组，每个用户最多回答 5 个功能点，且尽量是区分度大的功能点。</p>
<p>③ 在题型上，建议优先选择单选题，避免使用阵列题，因为阵列题下，用户更容易乱答或者回答得没有区分度，导致最终各个功能点没有区分度，如都属于期望功能。</p>
<p>④ 功能的解释：简单描述该功能点，确保用户理解；</p>
<p>⑤ 选项说明：由于用户对“我很喜欢”“理应如此”“无所谓”“勉强接受”“我很不喜欢”的理解不尽相同，因此需要在问卷填写前给出统一解释说明，让用户有一个相对一致的标准，方便填答。</p>
<ul>
<li> 我很喜欢：让你感到满意、开心、惊喜。</li>
<li> 它理应如此：你觉得是应该的、必备的功能 / 服务。</li>
<li> 无所谓：你不会特别在意，但还可以接受。</li>
<li> 勉强接受：你不喜欢，但是可以接受。</li>
<li> 我很不喜欢：让你感到不满意。</li>
</ul>
<p>⑥ 增加效标以用于验证 KANO 结果或分人群分析：用户使用功能的频率（后台数据、问卷询问）、用户最喜欢哪个功能（如果各个功能点区分度小时，如都属于期望属性，可从喜爱度上再进行二次划分）、用户会因为哪个功能而选择使用该产品。</p>
<p><strong>2.2 数据分析 </strong></p>
<p> 数据清洗→KANO 二维属性归属分析→Better-Worse 系数计算。可以直接在 Excel 或 SPSS 中进行分析。</p>
<p> 此外，还可以结合产品的一些数据支持进行结合分析，如用户画像，UV，转化率等。</p>
<p><strong>2.3 数据解读 </strong></p>
<p>KANO 模型是对功能 / 服务的优先级进行探索，具体情况还需要和业务方进行讨论，将 Kano 模型结果和业务实际情况结合讨论确定可行的产品功能开发 / 优化的优先级顺序，以将调研结果落地实施。</p>
<blockquote>
<p> 本文转载自 <a target="_blank" rel="external nofollow noopener noreferrer" href="https://www.zhihu.com/question/22989667/answer/61109592">https://www.zhihu.com/question/22989667/answer/61109592</a></p>
</blockquote>

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